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10 - Applying artificial neural networks to the study of prey colouration

from Part III - Artificial neural networks as models of perceptual processing in ecology and evolutionary biology

Published online by Cambridge University Press:  05 July 2011

By
Sami Merilaita
Edited by
Colin R. Tosh  and
Graeme D. Ruxton
Sami Merilaita
Affiliation:
Åbo Akademi University
Colin R. Tosh
Affiliation:
University of Leeds
Graeme D. Ruxton
Affiliation:
University of Glasgow
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Summary

Introduction

In this chapter I will examine the use of artificial neural networks in the study of prey colouration as an adaptation against predation. Prey colouration provides numerous spectacular examples of adaptation (e.g. Cott, 1940; Edmunds, 1974; Ruxton et al., 2004). These include prey colour patterns used to disguise and make their bearers difficult to detect as well as brilliant colourations and patterns that prey may use to deter a predator. As a consequence, prey colouration has been a source of inspiration for biologists since the earliest days of evolutionary biology (e.g. Wallace, 1889).

The anti-predation function of prey colouration is evidently a consequence of natural selection imposed by predation. More specifically, it is the predators' way of processing visual information that determines the best possible appearance of the colouration of a prey for a given anti-predation function and under given conditions. Because predators' ability to process visual information has such a central role in the study of prey colouration, it follows that we need models that enable us to capture the essential features of such information processing.

An artificial neural network can be described as a data processing system consisting of a large number of simple, highly interconnected processing elements (artificial neurons) in an architecture inspired by biological nerve systems (Tsoukalas & Uhrig, 1997). Artificial neural networks provide a technique that has been applied in various disciplines of science and engineering for tasks such as pattern recognition, categorisation and decision making, as well as a modelling tool in neural biology (e.g. Bishop, 1995; Haykin, 1999).

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Modelling Perception with Artificial Neural Networks , pp. 215 - 235
Publisher: Cambridge University Press
Print publication year: 2010

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